In Phase 1, our team was able to enhance the ABM B-Alert system for use in on the road evaluations by reducing the overall profile of the hardware system, introducing real-time commenting for marking events in real world activities, and assessing (and confirming) current artifact rejection algorithms for sufficiency in real world environments. In addition, the B-Alert algorithms for engagement, distraction/drowsiness, and workload were validated for use in the HIV+ population, and applied in laboratory-based cognitive assessments, simulated driving and on the road driving. The data from Phase 1 indicate that traditional clinic/laboratory assessments, which have been only modestly successful in predicting real-world behaviors, can be enriched by the addition of EEG metrics, and that this may substantially improve the prediction of real-world driving difficulties. Initial ata suggest that the predictors of on-road driving may vary by HIV status, and that age may be a factor as well. Of note, HIV is now considered a chronic, manageable condition in individuals who have access to treatment. Approximately one quarter of HIV infected (HIV+) persons in the U.S. are 50 years or older, with prevalence rates in this age group expected to reach 50% by 20153. While HIV-associated neurocognitive disorders (HAND) are not as severe as in previous generations1, they remain prevalent and can impact everyday functioning, including automobile driving. As with other conditions, clinic-based NP assessments only modestly predict success or failure at real world tasks. Given these factors, in Phase II we propose to emphasize the application of EEG metrics in older HIV+ individuals. The aims of this proposal are to: 1) improve the ease of use of the B-Alert system within the simulation to ensure that assessment is easily executed, with minimal training, 2) develop focused driving simulations that better reflect real-world challenges (e.g., in-car tasks) and contribute to more robust simulator-based EEG data collection, and 3) extend the current findings to a larger sample focused on aging HIV participants, who will complete both laboratory simulations and on-the-road evaluations. This project will occur in two parallel branches. The first branch will involve improving the ease of us of the B-Alert system and expanding the neurocognitive assessments for more driving specific skills (divided attention, useful field of view), and modifying the simulations based on Phase 1 experience (designing screening, and evaluation, simulations). To optimize ease of use, data quality monitoring options (e.g., window in window displays with STI software, remote notification of issues such as a belt buzzer, or pop up on a remote screen) will be developed, along with one-step applications with dry-gel sensor interface. The second branch will expand our Phase 1 results by focusing on an aging population and increasing the sample size to 50 HIV+ and 50 HIV- drivers > 55 years of age. All participants will complete the cognitive assessments and simulations, and half of each of group will complete the on-the-road assessments. As part of this branch, we will also leverage the development of semi-dry electrodes to evaluate the utility of these sensors for data quality in real-world applications. Participants will be recruited from the HIV Neurobehavioral Research Center and Owen HIV Clinic at UCSD. If successful, the proposed tool would 1) provide researchers with a new method for assessing components of real-world functioning, 2) validate the first on-road driving cognitive state algorithms, and 3) develop predictors of on-road driving impairments, reducing the need for potentially dangerous real-life driving assessments, The envisioned final product would a) allow for integration of in-laboratory and in-the-wild assessments for a variety of real world applications, b) be available for use by researchers, clinicians, and public safety officials and c) be relevant to a broad range of conditions, including aging and various neurologic (e.g., stroke, TBI recovery) and psychiatric (e.g., substance use) disorders.